While still at an early pre-hardware release stage, it is possible to draw some preliminary conclusions based on an analysis of the MIC and GPU architectures and the currently available information about the NVIDIA Kepler and Intel Knights Corner chips. In this article, I consider these two processors based on established high-level comparative measures such as memory capacity, balance ratios, and Amdahl’s Law in the context of four programming paradigms:

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Industry Perspectives

Over at the IBM Blog, IBM Fellow Hillary Hunter writes that the company anticipates that the world’s volume of digital data will exceed 44 zettabytes, an astounding number. "IBM has worked to build the industry’s most complete data science platform. Integrated with NVIDIA GPUs and software designed specifically for AI and the most data-intensive workloads, IBM has infused AI into offerings that clients can access regardless of their deployment model. Today, we take the next step in that journey in announcing the next evolution of our collaboration with NVIDIA. We plan to leverage their new data science toolkit, RAPIDS, across our portfolio so that our clients can enhance the performance of machine learning and data analytics." [Read More...]

White Papers

Both hardware and software advances in deep learning (DL), a type of ML, appear to be catalysts for the early stages of a phenomenal AI growth trend. Download the new white paper from NVIDIA that addresses the challenges described in PLASTER, which is important in any deep learning solution, and it is especially useful for developing and delivering the inference engines underpinning AI-based services.